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(bright upbeat music)


Welcome, my name is Lou Brooks.


I'm the Vice President


of Commercial Analytics here at Optum


and today I'm going to talk to you


about creating a clearer picture of the patient experience


through the integration of claims


and electronic health record data.


We're all well aware that the health ecosystem continues


to evolve as we collectively try to manage


the cost of healthcare,


develop new compounds to treat diseases


and get consumers to take a promo more proactive role


in managing their health.


We continue to drive innovation in the areas


of drug development, health technology, surgery,


care models, reimbursement and analytics,


but much of this innovation is based upon


existing data strata utilizing either claims


or electronic health record data in isolation.


We have the opportunity today


to further accelerate our understanding of healthcare


and drive noble changes


to many of our fundamental constructs


by altering the underlying data foundation


to many of our processes.


It starts quite simply by integrating claims


and electronic health record data at scale


to provide a unique data foundation


that enables us to see a more comprehensive view


of patient care, outcomes and costs,


become more tailored in addressing healthcare questions


in a more cost effective manner


with a single source of data,


and improve on our existing models and methods


to shift our focus to joint clinical and cost outcomes,


to identify those treatments and care pathways


that lead to the best clinical outcomes


for our patients at the lowest possible cost.


How does it all begin?


When we think about


the data itself,


from that standpoint,


on the next slide,


you'll see that we have a wide range


of data potentially available to us.


As we continue to work to transform healthcare,


a perfect example of that transformation


is machine learning models


and identifying patients that may be at risk


for a certain condition


and proactively intervening with those patients


to positively alter their health trajectory.


However, even the best of


those models miss a fairly large portion


of the appropriate patients and falsely identify others,


leading to some level of healthcare waste


and missed opportunities


to positively engage with patients on their health.


Why is that?


Many of the models today are based solely on claims


or solely on clinical data.


It's like using unleaded gasoline


when premium is needed in a sports car.


It runs, gets the job done,


but it isn't firing on all cylinders optimally,


so you aren't quite getting peak performance.


Integrating gait as the premium gasoline


for that sports car,


and it doesn't just stop at integrating claims


and electronic health record data.


As this graphic shows,


there's a wide range of additional data available


to bring into healthcare research.


The ultimate goal for healthcare researchers


is to obtain that comprehensive view of the patient,


including healthcare data, attitudinal information,


consumer profiling and purchase data


and leveraging all of those elements at once


to truly understand health outcomes and costs.


We aren't there yet today,


but the integration of claims


and EHR data is a big step in that direction.


Imagine for a moment how


integrated claims electronic health record data


could change the way we engage


in existing analysis across


the entire spectrum of healthcare.


Let's start by imagining being able


to fully assess a clinical trial protocol


for inclusion and exclusion criteria,


and then be able to drive


that directly back to site selection.


How many times have you used claims data


to engage in that process


only to be unable to identify the very specific criteria


that's essential to your protocol,


or be really targeted


at your protocol assessment using clinical data,


but be unable to identify the best sites


for potential trials.


The integration of data, claims


and electronic healthcare record data,


has the potential to support the entire work stream


with one set of data.


Let's move forward a bit and talk


about comparative-effectiveness studies.


Most of that work today is done with claims,


data and various methods analyzing either total cost of care


or the cost of the disease of interest.


How much better could those models be


if we were able to integrate clinical information


in case matching methods


and stratify the analysis by disease severity


or other clinical metrics of interest


that could provide additional illumination


and insights on product performance.


Integrated data also has the potential


to change the way we evaluate product performance over time.


Imagine for a moment,


being able to stratify patients being put on your drug


by clinical severity, measure with lab results,


and then tracking the clinical performance


and cost of those patients over time.


Imagine that information being available at your fingertips


when you sit down with a payer


to discuss the total cost of care


of the patients on your drug compared to competitors.


How could that change that discussion?


Both clinical and claims data


have their own individual


strengths and weaknesses.


Claims data is missing critical clinical data elements.


Things like lab results, observations,


notes information where a doctor


has recorded specific pieces of information on care


that have occurred during that particular instance.


Those clinical insights could provide a better understanding


of what's going on from a care management standpoint.


On the electronic health record front,


we're missing claims,


data information such as costs, eligibility,


and filled prescriptions.


As this graphic illustrates in that gray box in the middle,


we've got a great deal of data in common to both,


but the integration of the data allows us


to gain access to all the information.


As a result of that,


we fill in missing pieces of information


from either of the individual unique sources,


resulting in a new data foundation for healthcare analytics.


Let's look at an example.


Very simple, very straightforward,


but it gives you some insight as to what happens


when you can integrate claims


and electronic health record data together


from an analytical perspective.


Let's imagine that we have a patient


who has visited the emergency room.


With claims data,


we know that they had a claim for an outpatient visit,


they went to the emergency room,


we saw that it was billed related to diabetes


and the cost was about $7,000.


With EHR data,


we can easily confirm the visit and the diagnosis


because those data elements are in common


to both sources of data.


In EHR data however,


provides a wider range of additional data on the patient


that would be unseen


in just a straight-claims-based-analysis.


Including basic observational data such as height,


weight, blood pressure,


as well as extremely high A1C level,


and what medications were prescribed to the patient.


This additional information only scratches the surface


of what's available


from an electronic health record perspective,


including things like symptomology,


patient reported medications


and other health data


that will provide context


around the specific interaction


and the specific treatment decisions.


Let's go back to claims for a moment.


With that integration of data,


we're now able to connect that fill information


from the pharmacy,


with the electronic health record information


and see that the patient actually followed through


with the three written prescriptions


from that emergency room visit


and filled those three prescriptions.


And this journey can continue from there,


and you can demonstrate a wide range of impacts


from a treatment decision standpoint with providers.


We'll talk a little bit more about that in a moment.


The integration of data does come with its challenges


and its limits.


Perhaps the biggest three challenges are first, sample size.


The more disparate data sources we integrate together,


the smaller the sample of data that you have to work with.


So it's essential to work with the largest sets


of individual claims and electronic health record data,


to give you that largest intersection you can


from an analysis standpoint.


Completeness can also be difficult.


On the claim side,


many claims sources are eligibility controlled,


so you know what you're missing.


But as you start to integrate various sources


of electronic health record data,


you may or may not be missing particular pieces of data.


The same can hold true with claims sources,


depending on whether or not they're open or closed sources.


The final challenge is really around resource competency.


You need to rethink the way that you model


and you analyze data once you start integrating it.


The old claims based algorithms don't hold true perfectly


and that research that you've been doing


from a clinical perspective, isn't perfect either.


You have to rethink the way


that you are working with the data


and your researchers need to be retrained.


We must also remember


that privacy is a fundamental component


and a responsibility of all of us


as we're working with these integrated data source.


While it doesn't stop the integration of data,


it does complicate it


and it complicates what you can do with it.


You need to make sure


that we're working to look at that data


and make sure that we are hyper compliant as we work


through all of this integration standpoint.


Because, while it limits what we can do,


it doesn't prevent outright analytics


with that integrated data source.


So let's change gears now.


We've talking theoretical for the last 10 or 15 minutes,


let's move into some actual use cases


to show you what we can do with integrated data.


I've got a few examples of how


that integrated data can be utilized,


translated into analytic value


and change how we generate insights.


The first example is perhaps the easiest.


It's the "low- hanging fruit".


And we've touched briefly upon it


with the emergency room example previously.


The integrated data provides a one stop shop


for truly understanding the journey


of a patient to get treatment from that interaction


and written prescription in the office,


through the subsequent fill and refills


of those prescriptions over time.


In this example,


we get to see a piece of information


that isn't normally available in a claims analysis.


That information is


the actual written prescription or order.


The physician's intended treatment for that patient.


Once we have that information,


we can move to claims.


Claims now allows us to cycle


through the administrative process


of filling the prescription


from the point of presentation to the pharmacy,


you through the utilization management programs


that might exist


and the fill and subsequent pickup by the patient.


The claims data also allows us


to track subsequent prescriptions and adherence,


and if treatment changes do occur,


the electronic health record data gives us


those reasons for change


such as the example in the lower right hand of your screen.


So as we can demonstrate with the three product examples,


we can see


not only how many prescriptions were originally written,


how many of those were presented to the pharmacy


and ultimately how many of those got into


the hands of the individual patient.


Having insight along this pathway offers many opportunities


for all of us providers, payers,


and life savings companies alike,


to develop and target programs to maximize


the number of patients


that they're get their prescriptions


and stay on their medications.


Imagine for a moment,


just one simple example.


A provider has access to the integrated data.


They can follow a patient who doesn't present


that written prescription to the pharmacy


and contact them to discuss treatment again,


and depending on the reason why


that patient decided not to present the prescription,


work to overcome that barrier to filling


and getting that patient treated.


Now let's look at a integration of the data,


utilizing both clinical metrics and cost.


We're going to look at the correlation between cost


and clinical outcomes


for type 2 diabetes patients.


We took a very simple approach to this,


identifying type 2 diabetics in 2016


and calculating their baseline,


A1c and BMI levels at the end of 2016.


We then had a year's worth of data for them throughout 2017,


and we tracked all of their healthcare interactions


and expenditures over the course of 2017,


and we deciled them.


Decile one being the most expensive or highest cost segment,


and decile 10 being the lowest.


The 80-20 rule does hold true,


so you'll know that the smaller deciles


are in the higher costs of one, two, three, four, and five,


and the larger segment memberships are


in deciles eight, nine and 10.


We then looked at their clinical metrics at the end of 2017,


to evaluate what the change was in those metrics


and how it related to cost.


We show that there's a positive correlation


between higher spend


and better clinical outcomes moving down


and to the left on the graphic.


But it is far from perfect correlation.


Just opens up a wide range of questions


and potential future analytic opportunities.


Imagine for a moment doing a comparative study


and being able to control for a wider range of confounders


and develop metrics on the most cost


and health outcome-effective therapies.


Imagine for a moment being able


to show a payer


your more cost-effective


and have a greater impact on clinical outcomes.


Imagine then being able to take that information


and build better pathways


to get a more impactful management of population health,


and a lowering of costs simultaneously.


We're going to get a little heavier


into the clinical metrics associated


with integrated data in this next example.


And our goal is to really evaluate


how integrated data can gain better insight


into the impacts of interventions.


Repeated health measures are a integral part


of the integrated data.


And they very succinctly allow us


to evaluate interventions based on


the actual elements of interest.


Let's take the case of bariatric surgery.


In the example in the lower left hand graphic


on your screen,


we have segmented bariatric surgery patients


over a one year period after surgery,


based on their BMI pre


and through out the entire year.


We know that the average cost


of a bariatric surgery runs about $27,000,but in this analysis we found that 8% of those surgeries


and 8% of the patients that received them,


saw no appreciable weight loss in the course of a year.


Imagine for a moment that we could build that


into some type of monitoring


to help alter that performance trajectory


and perhaps even change the performance metrics


to only reimburse physicians when we saw a positive gains


in healthcare outcomes related to the surgical intervention.


Let's take it a step further.


We subset these patients to just type 2 diabetics.


And we examined how they were able to manage


their type 2 diabetes during


that one year period post-bariatric surgery.


In the example on the right hand part of our screen,


we found that 64% of the patients


that were type 2 diabetic


and had bariatric surgery,


also had uncontrolled diabetes


during that one year period.


They also cost on average three times more than


the patients in the lower right hand part of that graphic,


that had the best performance in both weight loss,


and were better able to control their diabetes.


We've found that there were many differences using


the EHR data in healthcare engagement


between those groups as well.


Differences in engagement with providers,


diet, and exercise,


and the integration of the data allowed us


to see all of that.


And allows us now, to set to identify those issues,


evaluate the performance of these interventions,


set up new interventions, policies and protocols


to improve patient care overall.


A fourth used case is more targeted


towards life sciences companies,


and one of the greatest challenges that they face


in evaluating and understanding the investments


and the return that they they've gotten


from those investments in terms of product performance.


These companies spend millions,


hundreds of millions of dollars developing drugs,


and ultimately more marketing them


and bringing them into a space where we can utilize them


to improve the quality of life for our patients.


The problem is that monitoring many


of these markets is difficult because


the data itself resides in different silos.


Let's take the case of "The Immunology Space".


Truly understanding market share on a weekly


or even a monthly basis is difficult because


while many of the products go through the pharmacy channel,


still many others are administered in a physician's office


and paid through through the medical benefit.


Those two claims streams are independent


and they have different time lags associated with them.


Integrating claims and electronic health record data,


allows organizations to gain clarity in their markets


by combining all of those products together


in a single view in near real-time.


As I mentioned,


those medical claims tend to lag in some cases,


days, weeks, and even months,


which means that you can't evaluate properly market share


or product performance until all of


that information comes into play.


EMRs, and in this case EHRs,


are updated on a more routine basis and as a result,


we can integrate all of the data together


to give us all of the medically administered products


on the same case as the pharmacy products


and demonstrate market share in real-time.


Is to also opens up on the next slide,


the ability to track other things with the data as well,


looking at indications,


and why products are being prescribed from that perspective.


This type of market clarity on a weekly basis


can enable better reaction to competitive threats


and maximizing new opportunities for Life Science Companies.


Our last example


is all about tactics.


I spoke earlier about the resources that you need.


You need to be able to change them


as you work with integrated data.


And it's likely the case today


that your organization may not yet be at a point where


you can tactically implement programs, pathways,


or other strategies using integrated data.


It may be that you only have claims data


to leverage for your operations,


or perhaps only clinical data.


If you recall in my earlier in my presentation,


I showed you a graphic of the different types


of data found in EHR and claims,


and I noted a group of variables that are in common to both.


One of the advantages of integrated data is


you can build models using that common subset


to move from the strategic to the tactical,


even if you don't yet have the infrastructure in place


to leverage integrated data at scale for operations.


As we round this out,


I searched for a comment on healthcare data and change,


but was unsuccessful.


So I defaulted to one of my favorite quotes


about Charles Darwin.


And it really is,


is ultimately focused on change


and our ability to survive by being able to adapt to change.


As we are well aware,


as we are developing new models


and new methods to solve problems in healthcare,


integrated data will help fuel that change


and enable us to realize greater efficiency


and insight generation.


Imagine the ability to address value based


on clinical values and costs simultaneously.


Think of how we could develop new value based contracts,


reimbursement models and care pathways


all using one integrated source of data.


It is the future of healthcare analytics.


I hope you've found today's session informative,


and I thank you for your time.


(bright upbeat music)


Integrated data is the driver of change

Integrated data can help us realize greater efficiency, new reimbursement models and more proactive care pathways for better outcomes. Join Lou Brooks, vice president of Commercial Analytics at Optum, as he discusses how integrated data is ushering in the future of health care analytics.

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